Chlorophyll Fluorescence Imaging Research Articles

Photosystem II Tolerance to Excess Zinc Exposure and High Light Stress in Salvia sclarea L.

High light (HL) intensity has a substantial impact on light energy flow and partitioning within photosynthetic apparatus. To realize the impact of HL intensity on zinc (Zn) tolerance mechanisms in clary sage (Salvia sclarea L., Lamiaceae) plants, we examined the effect of the altered chlorophyll and nutrient uptake under excess Zn supply on the response mechanism of photosystem II (PSII) photochemistry. Eight-week-old clary sage plants were treated with 5 μM Zn (control) or 900 μM Zn in Hoagland nutrient solution. Leaf elemental analysis for Zn, Mn, Mg, and Fe was performed by inductively coupled plasma mass spectrometry (ICP-MS), whereas PSII functioning under HL was evaluated by chlorophyll fluorescence imaging analysis. Exposure of S. sclarea plants to 900 μM Zn increased leaf Zn accumulation and decreased leaf Mg and chlorophyll. The decreased non-photochemical quenching (NPQ) provided evidence of the photoprotection offered by the smaller light-harvesting antennae due to the reduced chlorophyll. The increased Mn after Zn exposure corresponded with higher efficiency of the oxygen-evolving complex (OEC) that was significantly correlated with the maximum efficiency of photosystem II (PSII) photochemistry (Fv/Fm). An increased electron transport rate (ETR) coincided with increased leaf Fe, which is known to play a vital role in the enzymes engaged in ETR. The decreased (32%) NPQ after an 8-day exposure to Zn caused an increased (10%) quantum yield of non-regulated energy loss in PSII (ΦNO), indicative of an increased singlet oxygen (1O2) production. It is suggested that the decreased NPQ induced acclimation responses of clary sage plants to HL and excess Zn by increasing 1O2 production. The reduced (18%) excess excitation energy (EXC) at PSII and the increased (24%) quantum yield of PSII photochemistry (ΦPSII) and ETR indicated improved photosynthetic efficiency under excess Zn and HL intensity. Therefore, the exposure of medicinal plants to excess Zn not only boosts their photosynthetic efficiency, enhancing crop yields, but can also improve Fe and Zn content, ameliorating the human health deficiency of these two essential micronutrients.

Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops.

The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.

LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union

BackgroundThe study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic.ResultsWe tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background.ConclusionThe method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.

Comparative Phytotoxicity of Metallic Elements on Duckweed Lemna gibba L. Using Growth- and Chlorophyll Fluorescence Induction-Based Endpoints.

In this study, we exposed a commonly used duckweed species-Lemna gibba L.-to twelve environmentally relevant metals and metalloids under laboratory conditions. The phytotoxic effects were evaluated in a multi-well-plate-based experimental setup by means of the chlorophyll fluorescence imaging method. This technique allowed the simultaneous measuring of the growth and photosynthetic parameters in the same samples. The inhibition of relative growth rates (based on frond number and area) and photochemical efficiency (Fv/Fo and Y(II)) were both calculated from the obtained chlorophyll fluorescence images. In the applied test system, growth-inhibition-based phytotoxicity endpoints proved to be more sensitive than chlorophyll-fluorescence-based ones. Frond area growth inhibition was the most responsive parameter with a median EC50 of 1.75 mg L-1, while Fv/Fo, the more responsive chlorophyll-fluorescence-based endpoint, resulted in a 5.34 mg L-1 median EC50 for the tested metals. Ag (EC50 0.005-1.27 mg L-1), Hg (EC50 0.24-4.87 mg L-1) and Cu (EC50 0.37-1.86 mg L-1) were the most toxic elements among the tested ones, while As(V) (EC50 47.15-132.18 mg L-1), Cr(III) (EC50 6.22-19.92 mg L-1), Se(VI) (EC50 1.73-10.39 mg L-1) and Zn (EC50 3.88-350.56 mg L-1) were the least toxic ones. The results highlighted that multi-well-plate-based duckweed phytotoxicity assays may reduce space, time and sample volume requirements compared to the standard duckweed growth inhibition tests. These benefits, however, come with lowered test sensitivity. Our multi-well-plate-based test setup resulted in considerably higher median EC50 (3.21 mg L-1) for frond-number-based growth inhibition than the 0.683 mg L-1 median EC50 derived from corresponding data from the literature with standardized Lemna-tests. Under strong acute phytotoxicity, frond parts with impaired photochemical functionality may become undetectable by chlorophyll fluorometers. Consequently, the plant parts that are still detectable display a virtually higher average photosynthetic performance, leading to an underestimation of phytotoxicity. Nevertheless, multi-well-plate-based duckweed phytotoxicity assays, combined with chlorophyll fluorescence imaging, offer definite advantages in the rapid screening of large sample series or multiple species/clones. As chlorophyll fluorescence images provide information both on the photochemical performance of the test plants and their morphology, a joint analysis of the two endpoint groups is recommended in multi-well-plate-based duckweed phytotoxicity assays to maximize the information gained from the tests.

Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images

Precision agriculture heavily relies on accurately segmenting wheat canopies from chlorophyll fluorescence (CHF) images. However, these images often face challenges due to inherent noise and illumination variations, primarily induced by the thermal activity of photons emitting a fluorescence effect. The unique nature of fluorescence introduces variations in illumination, especially during the crop's dark adaptation before experimentation. This adaptation aims to capture the full fluorescence effect, starting from minimum fluorescence and progressing to maximum fluorescence. In the initial stages of fluorescence, images tend to appear darker compared to those progressing towards maximum fluorescence. This variability necessitates the development of a sophisticated hybrid approach to eliminate noise and enhance contrast collaboratively, maximizing the benefits derived from CHF images. This paper introduces a novel hybrid preprocessing approach designed to address these challenges. The proposed method integrates five denoising techniques, namely Discrete Cosine Transform, Block Matching-3D, Low-Rank Matrix Approximation, Wiener Filtering, and Median Filtering, to mitigate the impact of noise in CHF images. Simultaneously, two enhancement techniques, Adaptive Histogram Specification and Gamma Correction, are employed to accentuate critical features, compensating for inherent variations in illumination during the fluorescence process. The hybrid preprocessing technique was proposed after analysing different combinations of denoising and enhancement techniques. Through qualitative and quantitative analysis of the results, it was observed that Block Matching-3D denoising with Gamma Correction produced the best output, with an Average PSNR of 0.54 and Average MSE of 0.07. This cascaded approach not only emphasizes noise reduction but also prioritizes the enhancement of crucial information within CHF images. By synergistically combining denoising and enhancement methods, the proposed approach optimizes the overall quality of the images, laying a foundation for improved wheat canopy segmentation. This research contributes a comprehensive and innovative solution to the challenges associated with CHF images in precision agriculture. The proposed hybrid approach holds promise for advancing the accuracy and reliability of wheat canopy segmentation, thereby enhancing the efficacy of precision agricultural practices. 

Microscale imaging sheds light on species-specific strategies for photo-regulation and photo-acclimation of microphytobenthic diatoms.

Intertidal microphytobenthic (MPB) biofilms are key sites for coastal primary production, predominantly by pennate diatoms exhibiting photo-regulation via non-photochemical quenching (NPQ) and vertical migration. Movement is the main photo-regulation mechanism of motile (epipelic) diatoms and because they can move from light, they show low-light acclimation features such as low NPQ levels, as compared to non-motile (epipsammic) forms. However, most comparisons of MPB species-specific photo-regulation have used low light acclimated monocultures, not mimicking environmental conditions. Here we used variable chlorophyll fluorescence imaging, fluorescent labelling in sediment cores and scanning electron microscopy to compare the movement and NPQ responses to light of four epipelic diatom species from a natural MPB biofilm. The diatoms exhibited different species-specific photo-regulation features and a large NPQ range, exceeding that reported for epipsammic diatoms. This could allow epipelic species to coexist in compacted light niches of MPB communities. We show that diatom cell orientation within MPB can be modulated by light, where diatoms oriented themselves more perpendicular to the sediment surface under high light vs. more parallel under low light, demonstrating behavioural, photo-regulatory response by varying their light absorption cross-section. This highlights the importance of considering species-specific responses and understanding cell orientation and photo-behaviour in MPB research.

Reduction of PSII photosynthetic performance by low temperature is the reason for the growing inhibition of Kobresia pygmaea Clarke

AbstractKobresia pygmaea Clarke is mainly distributed in the alpine region from 3800 to 4500 m elevation on the Qinghai-Tibet Plateau. It is excellent in low-temperature tolerance and trampling resistance and plays an important role in maintaining the stability of regional ecological environments. Mainly based on the chlorophyll fluorescence imaging technique, using grass sods blocks with a size of 20 × 20 cm2 collected from K. pygmaea forb meadow, simulated a low-temperature treatment in cultural room with 8 °C/4 °C (day/night) at diurnal photoperiod, we analyzed the effects of low temperature on PSII photochemical efficiency and non-photochemical quenching, as well as the interaction between low temperature and light intensity. The results showed that low temperature could significantly decrease the maximum quantum efficiency of PSII photochemistry (Fv/Fm) and inhibit the leaf growth of K. pygmaea. The light response curve of PSII relative linear electron transport rate was reduced by low temperature, while it recovered to almost normal level after two days of removing adversity. Further experiments under steady-state actinic light confirmed that light intensity was the main factor affecting the PSII photochemical efficiency, and its reduction caused by the low temperature showed a certain degree of reversibility. The non-photochemical quenching (NPQ) was significantly affected by light intensity, and its fast and slow components (NPQf and NPQs) in NPQ accounted for 36% and 64% of NPQ, respectively. The decrease in NPQf and corresponding increase in NPQs slowly recovered after relieving from low-temperature condition. The relative inhibition of photosynthesis (Pinh) was significantly enhanced by low temperature and reduced after removal of adversity. Strong light intensity could intensify the adverse effect of low temperature on PSII operating efficiency Fq′/Fm′ and efficiency factor Fq′/Fv′, as well as NPQ. In conclusion, low temperature was one of the main reasons limiting the photosynthetic performance of K. pygmaea on the Qinghai-Tibet Plateau and could aggravate photosynthetic photoinhibition. The adversity of low temperature frequently occurring in the plant growing season on the Qinghai-Tibet Plateau was constituted of the main factor of the low productivity level in alpine meadow.

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis.

Most agricultural land, as a result of climate change, experiences severe stress that significantly reduces agricultural yields. Crop sensing by imaging techniques allows early-stage detection of biotic or abiotic stress to avoid damage and significant yield losses. Among the top certified imaging techniques for plant stress detection is chlorophyll a fluorescence imaging, which can evaluate spatiotemporal leaf changes, permitting the pre-symptomatic monitoring of plant physiological status long before any visible symptoms develop, allowing for high-throughput assessment. Here, we review different examples of how chlorophyll a fluorescence imaging analysis can be used to evaluate biotic and abiotic stress. Chlorophyll a is able to detect biotic stress as early as 15 min after Spodoptera exigua feeding, or 30 min after Botrytis cinerea application on tomato plants, or on the onset of water-deficit stress, and thus has potential for early stress detection. Chlorophyll fluorescence (ChlF) analysis is a rapid, non-invasive, easy to perform, low-cost, and highly sensitive method that can estimate photosynthetic performance and detect the influence of diverse stresses on plants. In terms of ChlF parameters, the fraction of open photosystem II (PSII) reaction centers (qp) can be used for early stress detection, since it has been found in many recent studies to be the most accurate and appropriate indicator for ChlF-based screening of the impact of environmental stress on plants.

Microplastic pollution associated with reduced respiration in seagrass (Zostera marina L.) and associated epiphytes

Seagrasses provide crucial ecosystem services of relevance for the marine environment. However, anthropogenic activities are causing global seagrass decline. Increasing microplastic (MP) concentrations have been recognized as a novel threat to many marine organisms, but their effects on marine plants remain underexplored. Here, we investigate the effects of microplastic (polyethylene (PE) and polypropylene (PP)) exposure on the photosynthesis and respiration of the seagrass Zostera marina L. and its associated epiphytes. Measurements were conducted on seagrass leaves with and without epiphyte cover, as well as on epiphytes scraped off the leaf surface. Net gas exchange and pH drift measurements were used to determine rates of photosynthesis and respiration, as well as the ability of leaves and epiphytes to utilize bicarbonate. In addition, variable chlorophyll fluorescence imaging was employed to quantify the photosynthetic capacity of seagrass leaves. Our results show a limited effect of short-term (14 days) microplastic exposure on seagrass leaves and their associated epiphytes, although the photosynthetic activity and respiration rates were gradually reduced for bare seagrass leaves with increasing microplastic concentrations (25-1000 mg MP L-1). A >50% reduction in dark respiration of bare leaves was found at the highest MP exposure, while respiration rates of leaves with epiphytes and separated epiphytes were reduced by maximally ~45 and 30% upon MP exposure, respectively. Short-term microplastic exposure did not alter i) the ability to utilize bicarbonate, ii) the maximum quantum yield of PSII (FV/FM), nor iii) the light utilization efficiency of Z. marina leaves and associated epiphytes. The compensation irradiance decreased for all investigated specimens, and seagrass leaves (with and without epiphytes) were able to retain a positive net oxygen balance throughout all treatments. We speculate that the observed decrease in photosynthetic activity and respiration was caused by leachates from microplastics. Our findings thus indicate that seagrass Z. marina largely possess resilience toward microplastic pollution at its current level.

Bismuth exposure affects morpho-physiological performances and the ionomic profile in garden cress (Lepidium sativum L.) plants

Environmental pollution caused by heavy metals has long been considered a relevant threat to ecosystem survival and human health. The use of safer substitutes for the most toxic heavy metals in many industrial applications is discussed as a potential way to face this issue. In this regard, Bi has been proposed for replacing Pb in several production processes. However, few literature records reported on the effects of Bi on living organisms, particularly on plants. In this study, garden cress (Lepidium sativum L.) plants were exposed to different concentrations of Bi nitrate added to soil in growth chambers for 21 days. Results evidenced the toxic effect of Bi on shoot growth, regardless of the Bi nitrate concentration in the soil, paralleled by a similar reduction in the chlorophyll and carotenoid content, a decrease in the nitrogen balance index values, and an impairment of the photosynthetic machinery evaluated by chlorophyll fluorescence image analysis. The presence of Bi in the soil was shown to affect element accumulation in roots and translocation to shoots, with micronutrient content particularly reduced in the leaves of Bi-treated plants. A dose-dependent plant accumulation of Bi to metal concentration in the soil was observed, even if very low metal bioconcentration ability was highlighted. The reduced Bi translocation from roots to shoots in plants exposed to increasing Bi concentrations in the soil is discussed as a possible defense mechanism likely associated with the observed increase of anthocyan and flavonol contents and the activation of photoprotection mechanisms preventing higher damages to the photosynthetic apparatus.

Hormesis Responses of Photosystem II in Arabidopsis thaliana under Water Deficit Stress.

Since drought stress is one of the key risks for the future of agriculture, exploring the molecular mechanisms of photosynthetic responses to water deficit stress is, therefore, fundamental. By using chlorophyll fluorescence imaging analysis, we evaluated the responses of photosystem II (PSII) photochemistry in young and mature leaves of Arabidopsis thaliana Col-0 (cv Columbia-0) at the onset of water deficit stress (OnWDS) and under mild water deficit stress (MiWDS) and moderate water deficit stress (MoWDS). Moreover, we tried to illuminate the underlying mechanisms in the differential response of PSII in young and mature leaves to water deficit stress in the model plant A. thaliana. Water deficit stress induced a hormetic dose response of PSII function in both leaf types. A U-shaped biphasic response curve of the effective quantum yield of PSII photochemistry (ΦPSII) in A. thaliana young and mature leaves was observed, with an inhibition at MiWDS that was followed by an increase in ΦPSII at MoWDS. Young leaves exhibited lower oxidative stress, evaluated by malondialdehyde (MDA), and higher levels of anthocyanin content compared to mature leaves under both MiWDS (+16%) and MoWDS (+20%). The higher ΦPSII of young leaves resulted in a decreased quantum yield of non-regulated energy loss in PSII (ΦNO), under both MiWDS (-13%) and MoWDS (-19%), compared to mature leaves. Since ΦNO represents singlet-excited oxygen (1O2) generation, this decrease resulted in lower excess excitation energy at PSII, in young leaves under both MiWDS (-10%) and MoWDS (-23%), compared to mature leaves. The hormetic response of PSII function in both young and mature leaves is suggested to be triggered, under MiWDS, by the intensified reactive oxygen species (ROS) generation, which is considered to be beneficial for activating stress defense responses. This stress defense response that was induced at MiWDS triggered an acclimation response in A. thaliana young leaves and provided tolerance to PSII when water deficit stress became more severe (MoWDS). We concluded that the hormesis responses of PSII in A. thaliana under water deficit stress are regulated by the leaf developmental stage that modulates anthocyanin accumulation in a stress-dependent dose.

Photosynthetic capacity in seagrass seeds and early-stage seedlings of Zostera marina.

In many terrestrial seeds, photosynthetic activity supplies O2 to the developing plant embryo to sustain aerobic metabolism and enhance biosynthetic activity. However, whether seagrass seeds possess similar photosynthetic capacity to alleviate intra-seed hypoxic stress conditions is unknown. We used a novel combination of microscale variable chlorophyll fluorescence imaging, a custom-made O2 optode microrespirometry system and planar optode O2 imaging, to determine the O2 microenvironment and photosynthetic activity in developing seeds and seedlings of seagrass (Zostera marina). Developing, sheath-covered seeds exhibited high O2 concentrations in the photosynthetic active seed sheath and low O2 concentrations in the centre of the seed at the position of the embryo. In light, photosynthesis in the seed sheath increased O2 availability in central parts of the seed enabling enhanced respiratory energy generation for biosynthetic activity. Early-stage seedlings also displayed photosynthetic capacity in hypocotyl and cotyledonary tissues, which may be beneficial for seedling establishment. Sheath O2 production is important for alleviating intra-seed hypoxic stress, which might increase endosperm storage activity, improving the conditions for successful seed maturation and germination.

Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging.

Currently, mechanical and chemical damage is the main way to carry out weed control. The use of chlorophyll fluorescence (CF) technology to nondestructively monitor the stress physiological state of weeds is significant to reveal the damage mechanism of mechanical and chemical stresses as well as complex stresses. Under simulated real field environmental conditions, different species and leaf age weeds (Digitaria sanguinalis 2-5 leaf age, and Erigeron canadensis 5-10 leaf age) were subjected to experimental treatments for 1-7 days, and fluorescence parameters were measured every 24h using a chlorophyll fluorometer. The aim of this study was to investigate the changes in CF parameters of different species of weeds (Digitaria sanguinalis, Erigeron canadensis) at their different stress sites under chemical, mechanical and their combined stresses. The results showed that when weeds (Digitaria sanguinalis and Erigeron canadensis) were chemically stressed in different parts, their leaf back parts were the most severely stressed after 7 days, with photosynthetic inhibition reaching R=75%. In contrast, mechanical stress differs from its changes, and after a period of its stress, each parameter recovers somewhat after 1 to 2 days of stress, with heavy mechanical stress R=11%. Complex stress had the most significant effect on CF parameters, mainly in the timing and efficiency of changes in Fv/Fm, Fq'/Fm', ETR, Rfd, NPQ and Y(NO), with R reaching 71%-73% after only 3-4 days of complex stress, and its changes in complex stress were basically consistent with the pattern of changes in its chemical stress. The results of the study will help to understand the effects of mechanical and chemical stresses and combined stresses on CF parameters of weeds and serve as a guide for efficient weed control operations and conducting weed control in the future.

Detection of combined frost and drought stress in wheat using hyperspectral and chlorophyll fluorescence imaging

Immediate detection and prediction tools for interactive stresses are essential to avoid yield losses. This study investigated the capability of hyperspectral (HSI) and chlorophyll fluorescence imaging (CFI) to characterize individual and interactive frost (−4 °C) and drought (40% soil moisture content) stress responses at the booting stage of wheat under controlled environmental conditions. HSI full range reflectance (280–2500 nm), CFI, and traditional responses (leaf water content, photosynthesis, and enzymatic activity) were measured from flag leaves. The potentiality of HSI to detect cellular damage in terms of enzymatic activity was explored by developing partial least square regression (PLSR) models, and spectral indices were calculated to characterize the responses to individual and combined stresses. Reflectance in 360, 700, 1400, 1900, and 2460 nm wavebands showed the most important variables in developing PLSR models for enzymes. Superoxide dismutase, peroxidase, and ascorbate peroxidase showed the highest R2c (0.99, 0.95, and 0.98), R2v (0.94, 0.90, 0.93), and the ratio of prediction to deviation (4.04, 3.06, and 3.85). CFI decreased under individual frost and combined frost and drought stresses. Ratio vegetation stress index (RVSI), green difference vegetation index (GDVI), difference vegetation index (DVI), and normalized difference water index (NDWI) were the most important variables and could be used to detect combined frost and drought by determining enzymatic activity and reactive oxygen species (ROS), verified by PLSR models. Spectral indices and enzyme activities showed a strong correlation in determining yield losses. Hence, HSI and CFI techniques successfully detected combined frost and drought stresses for rapid quantification.

Waterlogging resistance and evaluation of physiological mechanism of three peach (Prunus persica) rootstocks.

Waterlogging occurs due to poor soil drainage or excessive rainfall. It is a serious abiotic stress factor that negatively affects crop growth. Waterlogging often causes plants to shed leaves, fruits, and, ultimately, to die. Peach (Prunus persica) trees are generally intolerant to waterlogging, and the primary peach rootstock used in Chinais "Maotao," which has very poor resistance to sensitivity. Therefore, waterlogging has become a restriction on the development of the peach industry in many regions. In this experiment, we tested the waterlogging resistance of "Maotao (Prunus persica (L.) Batsch)" (MT), "Shannong1 (GF677 × Cadaman)" (SN1), and "Mirabolano 29C (Prunus cerasifera)" (M29C) rootstocks. Using a simulated waterlogging method, the effects of waterlogging on the photosynthetic system, leaf pigments, osmotic adjustment, lipid membrane peroxidation, and antioxidant system of these three peach rootstocks were studied, and the changes of chlorophyll fluorescence parameters and fluorescence imaging were observed. The results showed that, with prolonged waterlogging, the photosynthetic pigment content and photosynthesis of the three peach rootstocks decreased rapidly, but the decomposition rate of SN1 and M29C chlorophyll was slower, and it still had high light energy absorption and energy transfer capabilities under waterlogging stress, which reduced the damage caused by waterlogging stress; under the stress of flooding, the osmoregulatory substances of the three rootstocks increased to varying degrees compared with normal conditions. At the same time, the enzyme activity of superoxide dismutase (SOD) activity, peroxidase (POD) activity, and catalase (CAT) activity in the leaves of the three rootstocks under flooding stress all increased and then decreased; during this period, malondialdehyde (MDA) continued to increase, and SN1 and M29C were significantly lower than MT; and chlorophyll fluorescence parameters, including the maximum photochemical efficiency (Fv/Fm), actual photochemical efficiency (ΦPSII), photochemical quenching coefficient (qP), non-photochemical quenching (NPQ), and electron transfer rate (ETR) decreased significantly. The tolerance of SN1 and M29C to waterlogging was significantly better than that of MT rootstocks. The rootstock and grafted seedlings of SN1 have good waterlogging tolerance.

Phenotyping drought tolerance and yield performance of barley using a combination of imaging methods

Drought resistance represents a complex of traits that are differently employed depending on drought type, severity and timing. Hence, a relatively comprehensive assessment of morphological and physiological phenotypic response to drought is required. We evaluated the dynamic responses of six barley genotypes, representing a wide range of drought tolerance, to continuous drying and re-watering using non-invasively measured parameters based on red-green-blue (RGB), thermal infrared and chlorophyll fluorescence imaging within an automated phenotyping platform. We identified three critical points in drought progress: i) 50% level of available soil water, ii) wilting point, iii) full plant recovery after re-watering. However, the individual monitored parameters showed the potential to evaluate drought sensitivity at different points since the onset of drying. The correlation with relative yield response gradually increased for the side projected leaf area (SPA) and reached the maximum at the point of full recovery. The actual quantum yield of photosystem II (ΦPSII) showed the highest correlation with a relative grain yield around wilting point. In contrast, the relative leaf temperature difference demonstrated a high correlation with yield response earlier, at 50% of available water. The highest correlations with the relative yield response were obtained for the colour RGB analysis at the wilting point and after recovery, particularly for khaki, beige, dark-green and olive-green colours. Multiple regression with parameters providing Pearson’s correlation coefficient R > 0.5 slightly improved the estimation of relative yield response to drought but ensured significant improvement of absolute grain yield estimation under drought stress. This study shows that combining the phenotyping methods representing different morphological and physiological traits allows not only the assessment of drought tolerance (based on relative yield response to drought) which is crucial for selecting the genetic resources for the subsequent breeding process but also allows to test the yield performance of new genotypes under drought stress.

Monitoring Drought Stress in Common Bean Using Chlorophyll Fluorescence and Multispectral Imaging.

Drought is a significant constraint in bean production. In this study, we used high-throughput phenotyping methods (chlorophyll fluorescence imaging, multispectral imaging, 3D multispectral scanning) to monitor the development of drought-induced morphological and physiological symptoms at an early stage of development of the common bean. This study aimed to select the plant phenotypic traits which were most sensitive to drought. Plants were grown in an irrigated control (C) and under three drought treatments: D70, D50, and D30 (irrigated with 70, 50, and 30 mL distilled water, respectively). Measurements were performed on five consecutive days, starting on the first day after the onset of treatments (1 DAT-5 DAT), with an additional measurement taken on the eighth day (8 DAT) after the onset of treatments. Earliest detected changes were found at 3 DAT when compared to the control. D30 caused a decrease in leaf area index (of 40%), total leaf area (28%), reflectance in specific green (13%), saturation (9%), and green leaf index (9%), and an increase in the anthocyanin index (23%) and reflectance in blue (7%). The selected phenotypic traits could be used to monitor drought stress and to screen for tolerant genotypes in breeding programs.

Chlorophyll Fluorescence Imaging for Early Detection of Drought and Heat Stress in Strawberry Plants.

The efficiency of photosynthesis in strawberry plants is measured to maintain the quality and quantity of strawberries produced. The latest method used to measure the photosynthetic status of plants is chlorophyll fluorescence imaging (CFI), which has the advantage of obtaining plant spatiotemporal data non-destructively. This study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm). The main components of this system include a chamber for plants to adapt to dark environments, blue LED light sources to excite the chlorophyll in plants, and a monochrome camera with a lens filter attached to capture the emission spectra. In this study, 120 pots of strawberry plants were cultivated for 15 days and divided into four treatment groups: control, drought stress, heat stress, and a combination of drought and heat stress, resulting in Fv/Fm values of 0.802 ± 0.0036, 0.780 ± 0.0026, 0.768 ± 0.0023, and 0.749 ± 0.0099, respectively. A strong correlation was found between the developed system and a chlorophyll meter (r = 0.75). These results prove that the developed CFI system can accurately capture the spatial and temporal dynamics resulting from the response of strawberry plants to abiotic stresses.

Bi-directional Monte-Carlo modelling of solar-induced chlorophyll fluorescence images for 3D vegetation canopies in the DART model

Remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) has a great potential for monitoring plant photosynthetic activity. Radiative transfer models (RTM) are essential to better interpret and extract information from SIF signals. DART is one of the most comprehensive and accurate 3D RTMs. Its standard mode DART-FT simulates SIF using a discrete ordinates method but is not adapted to large landscapes due to computational constraints. DART-Lux, the new mode based on a bi-directional path tracing algorithm, greatly improves DART computational efficiency for simulating images. This paper presents the theory of a novel SIF modelling algorithm in DART-Lux. We verified its accuracy with DART-FT and the SCOPE model for three types of canopies: turbid medium, maize field and forest. DART-Lux closely matches DART-FT (relative difference < 2%) with much better computational efficiency depending on the scene complexity, number of spectral bands and needed accuracy. For example, simulation time is reduced by a factor of ≈48, and memory usage by ≈50 for a maize field at 1 cm resolution. It allowed to simulate SIF images of large scenes as the 3×3km2 Ripperdan agricultural site that DART-FT could not simulate. The new SIF modelling algorithm opens new horizons for RS studies of large and complex landscapes. It is available as part of released DART versions (v1152 onwards) (https://dart.omp.eu/).

Study on the nitrogen content estimation model of cotton leaves based on "image-spectrum-fluorescence" data fusion.

Precise monitoring of cotton leaves' nitrogen content is important for increasing yield and reducing fertilizer application. Spectra and images are used to monitor crop nitrogen information. However, the information expressed using nitrogen monitoring based on a single data source is limited and cannot consider the expression of various phenotypic and physiological parameters simultaneously, which can affect the accuracy of inversion. Introducing a multi-source data-fusion mechanism can improve the accuracy and stability of cotton nitrogen content monitoring from the perspective of information complementarity. Five nitrogen treatments were applied to the test crop, Xinluzao No. 53 cotton, grown indoors. Cotton leaf hyperspectral, chlorophyll fluorescence, and digital image data were collected and screened. A multilevel data-fusion model combining multiple machine learning and stacking integration learning was built from three dimensions: feature-level fusion, decision-level fusion, and hybrid fusion. The determination coefficients (R2) of the feature-level fusion, decision-level fusion, and hybrid-fusion models were 0.752, 0.771, and 0.848, and the root-mean-square errors (RMSE) were 3.806, 3.558, and 2.898, respectively. Compared with the nitrogen estimation models of the three single data sources, R2 increased by 5.0%, 6.8%, and 14.6%, and the RMSE decreased by 3.2%, 9.5%, and 26.3%, respectively. The multilevel fusion model can improve accuracy to varying degrees, and the accuracy and stability were highest with the hybrid-fusion model; these results provide theoretical and technical support for optimizing an accurate method of monitoring cotton leaf nitrogen content.